Read more
Whether you're new to statistical analysis or looking to enhance your analytical skills with the R programming language, this textbook provides comprehensive and practical guidance for understanding fundamental statistical concepts through healthcare examples in R. It is an ideal resource for students, educators, and healthcare researchers seeking a step-by-step first approach to effectively applying R in the analysis of healthcare data.
Readers are introduced to the fundamentals of base R, along with practical methods for data import, preprocessing, and transformation using functions from standard R packages such as
base and
stats, as well as pipe-friendly functions from the
tidyverse collection of packages. Additionally, a chapter is devoted to visualization fundamentals, providing step-by-step guidance on creating data visualizations using the
ggplot2 package and its extensions.
The textbook covers the most common statistical tests (e.g., t-test, one-way ANOVA, chi-square test, correlation, and non-parametric tests) and introduces more specialized analyses (e.g., linear regression, survival analysis, reliability of measurement analysis, diagnostic test accuracy and ROC analysis) with examples from biomedical field. Basic mathematical equations for these statistical tests and techniques are provided to enhance understanding. Statistical functions from both "Base" R and the "rstatix" add-on package are often presented side-by-side, fostering engagement and enriching the reader's coding experience. Designed to be self-contained, this textbook does not require any prior experience with the R programming language, though it assumes a basic understanding of mathematics. (Note: Multivariable modeling and advanced statistical techniques are beyond the scope of this introductory textbook.)
Access the Support Material: https://osf.io/3amrb/files/osfstorage
List of contents
Preface About the Author 1 R via RStudio
2 RStudio Projects
3 R as calculator
4 R functions
5 R packages
6 R objects
7 Atomic vectors
8 Matrices and arrays
9 Lists and data frames
10 Data import, preprocessing, and transformation
11 Data visualization with ggplot2
12 Introduction to Statistics
13 Basic concepts of probability
14 Probability distributions
15 Descriptive statistics
16 Populations and samples
17 Confidence intervals
18 Hypothesis testing
19 Independent samples t-test
20 Wilcoxon-Mann-Whitney test
21 Paired samples t-test
22 Wilcoxon Signed-Rank test
23 One-way Analysis of Variance
24 Kruskal-Wallis test
25 Categorical data analysis
26 Correlation methods
27 Simple linear regression
28 Survival analysis
29 Reliability of measurement
30 Measures of diagnostic test accuracy
31 Receiver Operating Characteristic (ROC) curve
Bibliography Index
About the author
Konstantinos I. Bougioukas received his PhD in Biostatistics and Research Methodology from the Faculty of Medicine at Aristotle University of Thessaloniki, Greece, in 2021. He has extensive experience teaching both basic and advanced Statistics. He has mentored and guided students and researchers from diverse scientific backgrounds-including mathematics, medicine, biology, psychology, and health policy-in applying statistical methodologies and R programming. As a research methodologist and data analyst, he specializes in evidence synthesis-including systematic reviews and meta-analyses, overviews of reviews, and meta-epidemiological studies-as well as advanced data analysis and data visualization techniques. He has authored over 40 peer reviewed research articles published in high-impact biomedical journals. Additionally, he has contributed to the development of three open-source R packages: "ccaR", "amstar2Vis", and "musicolor".
